This paper describes an implementation of a complementary set of methods called stochastic quasi-gradient methods (SQG). These methods are specifically designed having in mind continuous distributions of random parameters and nonlinear optimization problems. They are suited for optimization of simulation models where analytical relations between the objective function and parameters are difficult to trace. Another application area of such methods consists of multiperiod dynamic stochastic models with parametrized decision rules. Supply chain management, financial applications, telecommunications, and energy generation are among the sources of such problems. The most important notion here is the stochastic gradient, which is a statistical estimate of the gradient of the objective function.

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  4. Ermoliev, Yuri; von Winterfeldt, Detlof: Systemic risk and security management (2012)
  5. Gaivoronski, Alexei A.; Werner, Adrian: Stochastic programming perspective on the agency problems under uncertainty (2012)
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